Sensitivity analysis of GLEAMS model using a multiobjective sensitivity analysis procedure

2003 ◽  
Author(s):  
Adion J. Chinkuyu ◽  
Tom Meixner ◽  
Timothy J. Gish ◽  
Craig S. Daughtry
Keyword(s):  
Sensitivity Analysis ◽  
Analysis Procedure ◽  
Gleams Model

scholarly journals A sensitivity analysis procedure for Bayesian decision-making

2009 ◽  
Vol 1 (1/2) ◽  
pp. 23
Author(s):  
Faizul Huq ◽  
Clarence 'Red' Martin ◽  
Ken Cutright ◽  
Trevor S. Hale
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Analysis Procedure ◽  
Bayesian Decision ◽  
Bayesian Decision Making

Uncertainties in Predictions by Thermal-Hydraulic Codes: Approaches and Results

2010 ◽  
Author(s):  
A. Petruzzi ◽  
F. D’Auria
Keyword(s):  
Complex System ◽  
Experimental Data ◽  
Sensitivity Analysis ◽  
Measured Data ◽  
Probability Density Functions ◽  
Analysis Procedure ◽  
Density Functions ◽  
Uncertain Parameters ◽  
Industrial Level ◽  
Selection Of

The present paper deals with the description of the salient features of three independent approaches for estimating uncertainties associated with predictions of complex system codes. The 1st approach is the “standard” one and the most used at the industrial level: it is based upon the selection of input uncertain parameters, on assigning related ranges of variations and, possibly, PDF (Probability Density Functions) and on performing a suitable number of code runs to get the combined effect of variation on the results. In the 2nd approach the uncertainty derives from the comparison between relevant measured data and results of corresponding code calculations. The 3rd approach is based upon a sensitivity analysis procedure and uses the experimental data to characterize the ranges of variation of ‘all’ input parameters. Selected results from the application of the 2nd approach are outlined.

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